Numerous implantable medical devices (IMDs) are configured for monitoring and storing physiological data for use in diagnosing a patient condition or managing medical therapies. Such devices include implantable cardiac pacemakers, implantable cardioverter defibrillators (ICDs), hemodynamic monitors, subcutaneous ECG monitors, neural stimulators, and the like. An IMD may be capable of detecting numerous types of physiological events based on sensed signals but generally has limited memory capacity due to physical size restraints for storing data relating to detected physiological events. Detection of a physiological event, such as an arrhythmia, may trigger storage of physiological signal data in an IMD. When the memory available for physiological data storage is full, previously stored event episodes may be overwritten with newer events, resulting in a loss of some data.
Older data that is overwritten may correspond to severe or highly clinically significant data. To address this potential loss of valuable data, methods have been proposed for prioritizing data that is stored such that older data is overwritten only when new data is determined to be higher priority data. However, a limitation remains in that a clinician may be unaware what types of physiological events a patient may be experiencing and may therefore not program an implanted device to monitor and store data relating to physiological events that might be important in properly diagnosing and treating the patient. The IMD may store physiological event data corresponding to one type of event while other physiological events go unnoticed or remain poorly documented.